tf.foldr

tf.foldr(
    fn,
    elems,
    initializer=None,
    parallel_iterations=10,
    back_prop=True,
    swap_memory=False,
    name=None
)

Defined in tensorflow/python/ops/functional_ops.py.

See the guide: Higher Order Functions > Higher Order Operators

foldr on the list of tensors unpacked from elems on dimension 0.

This foldr operator repeatedly applies the callable fn to a sequence of elements from last to first. The elements are made of the tensors unpacked from elems. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initializer is None, elems must contain at least one element, and its first element is used as the initializer.

Suppose that elems is unpacked into values, a list of tensors. The shape of the result tensor is fn(initializer, values[0]).shape.

Args:

• fn: The callable to be performed.
• elems: A tensor that is unpacked into a sequence of tensors to apply fn.
• initializer: (optional) The initial value for the accumulator.
• parallel_iterations: (optional) The number of iterations allowed to run in parallel.
• back_prop: (optional) True enables support for back propagation.
• swap_memory: (optional) True enables GPU-CPU memory swapping.
• name: (optional) Name prefix for the returned tensors.

Returns:

A tensor resulting from applying fn consecutively to the list of tensors unpacked from elems, from last to first.

Raises:

• TypeError: if fn is not callable.

Example:

elems = [1, 2, 3, 4, 5, 6]
sum = foldr(lambda a, x: a + x, elems)
# sum == 21